Unless otherwise indicated herein, the approaches described in this section are not prior art to the claims in this application and are not admitted to be prior art by inclusion in this section.
In practice, many real-world datasets are multi-dimensional and include multiple variables. While multi-variable databases are good for showing correlation and relationships between different variables, there is a problem of presenting the data in a format that is easy for the user to process and consume. For example, a relational database can include a count of all the flowers in a garden based on the type of flower and the number of petals the flower has. Thus a count can be maintained for each combination of the two variables; type and petal number. Due to the amount of information present, it can be challenging for users to gain an overview of the data in a moderately sized database. Users must typically choose some small subset of dimensions (such as flowers of a specific petal number) or variables of interest (such as specific types of flowers) before visualizing the data. However, this traditional form of visualization does not provide an overview of all the multiple dimensions simultaneously.